Polars: Index page

docs/connect/df/index.md

@@ -86,75 +86,17 @@ and operations for manipulating numerical tables and time series.
 - [From data storage to data analysis: Tutorial on CrateDB and pandas]
 
 
-
-(polars)=
 ## Polars
-
-:::{rubric} About
-:::
-
-```{div}
-:style: "float: right; margin-left: 0.5em"
-[![](https://github.com/pola-rs/polars-static/raw/master/logos/polars-logo-dark.svg){w=180px}](https://pola.rs/)
-```
-
-[Polars] is a blazingly fast DataFrames library with language bindings for
-Rust, Python, Node.js, R, and SQL. Polars is powered by a multithreaded,
-vectorized query engine, it is open source, and written in Rust.
-
-- **Fast:** Written from scratch in Rust and with performance in mind,
-  designed close to the machine, and without external dependencies.
-
-- **I/O:** First class support for all common data storage layers: local,
-  cloud storage & databases.
-
-- **Intuitive API:** Write your queries the way they were intended. Polars,
-  internally, will determine the most efficient way to execute using its query
-  optimizer. Polars' expressions are intuitive and empower you to write
-  readable and performant code at the same time.
-
-- **Out of Core:** The streaming API allows you to process your results without
-  requiring all your data to be in memory at the same time.
-
-- **Parallel:** Polars' multi-threaded query engine utilises the power of your
-  machine by dividing the workload among the available CPU cores without any
-  additional configuration.
-
-- **Vectorized Query Engine:** Uses [Apache Arrow], a columnar data format, to
-  process your queries in a vectorized manner and SIMD to optimize CPU usage.
-  This enables cache-coherent algorithms and high performance on modern processors. 
-
-- **Open Source:** Polars is and always will be open source. Driven by an active
-  community of developers. Everyone is encouraged to add new features and contribute.
-  It is free to use under the MIT license.
-
-:::{rubric} Data formats
-:::
-
-Polars supports reading and writing to many common data formats.
-This allows you to easily integrate Polars into your existing data stack.
-
-- Text: CSV & JSON
-- Binary: Parquet, Delta Lake, AVRO & Excel
-- IPC: Feather, Arrow
-- Databases: MySQL, Postgres, SQL Server, Sqlite, Redshift & Oracle
-- Cloud Storage: S3, Azure Blob & Azure File
-
-```{div}
-:style: "clear: both"
-```
-
-:::{rubric} Learn
+:::{seealso}
+Please navigate to the dedicated page about {ref}`polars`.
 :::
-- [Polars code examples]
 
 
 [Apache Arrow]: https://arrow.apache.org/
 [Dask]: https://www.dask.org/
 [Dask DataFrames]: https://docs.dask.org/en/latest/dataframe.html
 [Dask Futures]: https://docs.dask.org/en/latest/futures.html
 [pandas]: https://pandas.pydata.org/
-[Polars]: https://pola.rs/
 
 [Dask code examples]: https://github.com/crate/cratedb-examples/tree/main/by-dataframe/dask
 [Efficient batch/bulk INSERT operations with pandas, Dask, and SQLAlchemy]: https://cratedb.com/docs/python/en/latest/by-example/sqlalchemy/dataframe.html
@@ -164,4 +106,3 @@ This allows you to easily integrate Polars into your existing data stack.
 [Import weather data using Dask]: https://github.com/crate/cratedb-examples/blob/main/topic/timeseries/dask-weather-data-import.ipynb
 [Importing Parquet files into CrateDB using Apache Arrow and SQLAlchemy]: https://community.cratedb.com/t/importing-parquet-files-into-cratedb-using-apache-arrow-and-sqlalchemy/1161
 [pandas code examples]: https://github.com/crate/cratedb-examples/tree/main/by-dataframe/pandas
-[Polars code examples]: https://github.com/crate/cratedb-examples/tree/main/by-dataframe/polars

docs/integrate/index.md

@@ -55,6 +55,7 @@ nifi/index
 node-red/index
 oracle/index
 plotly/index
+polars/index
 postgresql/index
 Power BI <powerbi/index>
 prometheus/index

docs/integrate/polars/index.md

@@ -0,0 +1,63 @@
+(polars)=
+# Polars
+
+```{div}
+:style: "float: right; margin-left: 0.5em"
+[![Polars logo](https://github.com/pola-rs/polars-static/raw/master/logos/polars-logo-dark.svg){w=180px}][Polars]
+```
+```{div} .clearfix
+```
+
+:::{rubric} About
+:::
+
+[Polars] is a high‑performance DataFrames library with interfaces for
+Rust, Python, Node.js, and R, plus a SQL context. It is powered by a
+multithreaded, vectorized query engine and written in Rust.
+
+- **Fast:** Written from scratch in Rust and with performance in mind,
+  designed close to the machine, and without external dependencies.
+
+- **I/O:** First class support for all common data storage layers: local,
+  cloud storage & databases.
+
+- **Intuitive API:** Write your queries the way they were intended. Polars,
+  internally, will determine the most efficient way to execute using its query
+  optimizer. Polars' expressions are intuitive and empower you to write
+  readable and performant code at the same time.
+
+- **Out of Core:** The streaming API allows you to process your results without
+  requiring all your data to be in memory at the same time.
+
+- **Parallel:** Polars' multi-threaded query engine utilizes the power of your
+  machine by dividing the workload among the available CPU cores without any
+  additional configuration.
+
+- **Vectorized Query Engine:** Uses [Apache Arrow], a columnar data format, to
+  process your queries in a vectorized manner and SIMD to optimize CPU usage.
+  This enables cache-coherent algorithms and high performance on modern processors. 
+
+- **Open Source:** Polars is and always will be open source. Driven by an active
+  community of developers. Everyone is encouraged to add new features and contribute.
+  It is free to use under the MIT license.
+
+:::{rubric} Data formats
+:::
+
+Polars supports reading and writing to many common data formats.
+This allows you to easily integrate Polars into your existing data stack.
+
+- Text: CSV, JSON
+- Binary: Parquet, Delta Lake, Avro, Excel
+- IPC: Feather, Arrow IPC
+- Databases: MySQL, PostgreSQL, SQLite, Redshift, SQL Server, etc. (via ConnectorX)
+- Cloud storage: Amazon S3, Azure Blob/ADLS (via fsspec‑compatible backends)
+
+:::{rubric} Learn
+:::
+- [Polars code examples]
+
+
+[Apache Arrow]: https://arrow.apache.org/
+[Polars]: https://pola.rs/
+[Polars code examples]: https://github.com/crate/cratedb-examples/tree/main/by-dataframe/polars